Result and analysis: TABLE REF  shows the comparison results. It is divided into 4 parts, which are the classic CNNs designed by hand, the CNNs searched by semi-automatic NAS, the CNNs searched by full-automatic NAS, and the two most popular networks based on attention mechanism, SE-ResNet-101 [1]} and CBAM-ResNet-101 [2]}. The symbol `-' means there is no result publicly reported by the corresponding competitor. The table also gives the parameters of these models. It can be inferred that the proposed ConAM outperforms all types of CNNs. ConAM's accuracy on CIFAR-10 [3]} and CIFAR-100 [3]} is 1.30 and 3.18 higher than the highest accuracy of the manually designed network, 94.78 and 77.70, respectively. Compared with the semi-automatic searched network, the accuracy of ConAM on CIFAR-10 [3]} and CIFAR-100 [3]} is 0.31 and 1.53 higher than the highest accuracy of 95.77 and 79.35, respectively. It is 0.38 and 1.73 higher than the best accuracy of the full-automatic searched network, 95.70 and 79.15 respectively. It is 0.33 and 1.62 higher than the highest values of the two networks based on the attention mechanism. It can be seen that ConAM performs better on more complex datasets. More importantly, in the attention group, not only is the performance of ConAM better than SE-ResNet-101 [1]} and CBAM-ResNet-101 [2]}, but also the number of parameters on CIFAR-10 [3]} and CIFAR-100 [3]} are about 4.7 M (10%) lower than that of SE-ResNet-101 [1]} and CBAM-ResNet-101 [2]}.
